Visualise hrp2 and hrp3 Deletions
Contents
Visualise hrp2 and hrp3 Deletions¶
Introduction¶
This notebook will create a public notebook for the generation of Figure 5 from the main Pf7 paper - a figure of HRP deletion breakpoints.
hrp2 and hrp3 are genes located in subtelomeric regions of the genome with very high levels of natural variation. Deletion in those genes can cause failure of rapid diagnostic tests and is therefore important to monitor.
Deletion is a genetic event in which a segment of DNA is entirely removed or missing. In this context, ‘breakpoints’ denote specific locations on the chromosome where such deletions take place.
This notebook should take approximately two minutes to run.
Setup¶
Install and import the malariagen Python package:
!pip install -q --no-warn-conflicts malariagen_data
import malariagen_data
Import required python libraries that are installed at colab by default.
import numpy as np
import pandas as pd
import collections
import matplotlib.pyplot as plt
from google.colab import drive
Access Pf7 Data¶
We use the malariagen data package to load the release data.
release_data = malariagen_data.Pf7()
df_samples = release_data.sample_metadata()
hrp2 & hrp3 Deletions¶
We additionally require list of deletion and breakpoint locations within the histidine-rich protein genes II and III (HRP-2 and -3) across 16,203 QC-pass samples. We can access this data from MalariaGEN web page.
# Fetch details of hrp calls from this MalariaGEN.net file
hrp_calls_fn = pd.read_csv('https://www.malariagen.net/wp-content/uploads/2024/01/hrp_calls_pf7.tsv', sep='\t')
# Print the shape and first rows
print(hrp_calls_fn.shape)
hrp_calls_fn.head()
(16203, 7)
| Sample | HRP2 | HRP3 | HRP2_breakpoint | HRP3_breakpoint | HRP2_deletion_type | HRP3_deletion_type | |
|---|---|---|---|---|---|---|---|
| 0 | FP0008-C | nodel | nodel | - | - | NaN | NaN |
| 1 | FP0009-C | nodel | nodel | - | - | NaN | NaN |
| 2 | FP0010-CW | uncallable | uncallable | - | - | NaN | NaN |
| 3 | FP0011-CW | uncallable | uncallable | - | - | NaN | NaN |
| 4 | FP0012-CW | nodel | nodel | - | - | NaN | NaN |
Now, let’s merge hrp_calls_fn with df_samples which contains various metadata of Pf7 samples.
# Merge df_samples with hrp_calls_fn
df_samples = df_samples.merge(hrp_calls_fn, on ='Sample')
3D7 Reference Annotation¶
Another relevant dataset in this notebook is the 3D7 reference annotation which is used for genome annotations in Pf7. It can be accessed at the following FTP link: ftp://ngs.sanger.ac.uk/production/malaria/Resource/34/Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff.
We will use the wget package to download this file. After running the following cell, the file should appear in the list of files on the left.
!wget ftp://ngs.sanger.ac.uk/production/malaria/Resource/34/Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff
--2024-02-08 11:27:43-- ftp://ngs.sanger.ac.uk/production/malaria/Resource/34/Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff
=> ‘Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff.1’
Resolving ngs.sanger.ac.uk (ngs.sanger.ac.uk)... 193.62.203.221
Connecting to ngs.sanger.ac.uk (ngs.sanger.ac.uk)|193.62.203.221|:21... connected.
Logging in as anonymous ... Logged in!
==> SYST ... done. ==> PWD ... done.
==> TYPE I ... done. ==> CWD (1) /production/malaria/Resource/34 ... done.
==> SIZE Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff ... 21035550
==> PASV ... done. ==> RETR Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff ... done.
Length: 21035550 (20M) (unauthoritative)
Pfalciparum_replace 100%[===================>] 20.06M 18.2MB/s in 1.1s
2024-02-08 11:27:45 (18.2 MB/s) - ‘Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff.1’ saved [21035550]
This file is in GFF file format defined by Ensembl.
Since the file is tab-separated, we can proceed to read the data into a Pandas dataframe once again.
# Read the file column names.
df_gff = pd.read_csv('/content/Pfalciparum_replace_Pf3D7_MIT_v3_with_Pf_M76611.gff', names=['chrom', 'db', 'type', 'start', 'end', 'u1', 'strand', 'u2', 'id'], comment='#', sep='\t')
# Print the shape (rows and columns) of df_gff
print(df_gff.shape)
# Show the first 5 rows
df_gff.head()
(40713, 9)
| chrom | db | type | start | end | u1 | strand | u2 | id | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Pf3D7_01_v3 | chado | repeat_region | 1 | 360 | . | + | . | ID=Pfalciparum_REP_20;comment=telomeric repeat... |
| 1 | Pf3D7_01_v3 | chado | repeat_region | 361 | 1418 | . | + | . | ID=Pfalciparum_REP_15;comment=14bp repeat |
| 2 | Pf3D7_01_v3 | chado | repeat_region | 2160 | 3858 | . | + | . | ID=Pfalciparum_REP_35;comment=65bp repeat |
| 3 | Pf3D7_01_v3 | chado | repeat_region | 8856 | 9021 | . | + | . | ID=Pfalciparum_REP_5;comment=25bp repeat |
| 4 | Pf3D7_01_v3 | chado | repeat_region | 9313 | 9529 | . | + | . | ID=Pfalciparum_REP_25;comment=26bp repeat |
Figure Preparation¶
We need to find the start and end positions of chromosomes to draw gene annotations in the figure that we are going to create.
# Find start and end positions for each chromosome by grouping chromosome coordinates
df_chroms = df_gff.groupby('chrom').agg({'start': 'min', 'end': 'max'}).reset_index()
# Set 'chrom' as the index
df_chroms.set_index('chrom', inplace=True)
df_chroms
| start | end | |
|---|---|---|
| chrom | ||
| Pf3D7_01_v3 | 1 | 640851 |
| Pf3D7_02_v3 | 1 | 947102 |
| Pf3D7_03_v3 | 1 | 1067971 |
| Pf3D7_04_v3 | 1 | 1200490 |
| Pf3D7_05_v3 | 1 | 1343557 |
| Pf3D7_06_v3 | 1 | 1418242 |
| Pf3D7_07_v3 | 1 | 1445207 |
| Pf3D7_08_v3 | 1 | 1472805 |
| Pf3D7_09_v3 | 1 | 1541735 |
| Pf3D7_10_v3 | 1 | 1687656 |
| Pf3D7_11_v3 | 1 | 2038340 |
| Pf3D7_12_v3 | 1 | 2260569 |
| Pf3D7_13_v3 | 1 | 2925236 |
| Pf3D7_14_v3 | 1 | 3291936 |
| Pf3D7_API_v3 | 1 | 34225 |
| Pf_M76611 | 3 | 5954 |
The next question is: How many samples have deletions in each country?
# This function returns samples with deletion for each country.
def breakpoint_agg(x):
names = collections.OrderedDict()
names['Countries'] = ''
countries = []
# Loop over each country
# Count non-zero samples (with deletion)
for country in x['Country'].unique():
countries.append(f"{country} ({np.count_nonzero(x['Country'] == country)})")
# Join together country name and number
names['Countries'] = ', '.join(countries)
names['Samples with deletion'] = len(x)
return pd.Series(names)
We will apply breakpoint_agg function to df_samples separately for hrp2 and hrp3.
Additionally, we will find genomic coordinates at the edges for mapping in the figure.
# Group samples by Deletion type and HRP3_breakpoint
# Apply breakpoint_agg to count samples with deletion in each country
df_hrp2 = (
df_samples[
df_samples['QC pass']
& ( df_samples['HRP2'] == 'del' )
]
.assign(Gene='$hrp2$')
.rename(columns={'HRP2_deletion_type': 'Deletion type'})
.groupby(['Deletion type', 'HRP2_breakpoint'])
.apply(breakpoint_agg)
.reset_index()
)
# Seperate coordinate value from chromosome
df_hrp2['breakpoint'] = df_hrp2['HRP2_breakpoint'].apply(lambda x: int(x.split(':')[1]))
# Print min and max coordinates
print(f"HRP2 min breakpoint = {df_hrp2['breakpoint'].min()}")
print(f"HRP2 max breakpoint = {df_hrp2['breakpoint'].max()}")
df_hrp2
HRP2 min breakpoint = 1373732
HRP2 max breakpoint = 1374986
| Deletion type | HRP2_breakpoint | Countries | Samples with deletion | breakpoint | |
|---|---|---|---|---|---|
| 0 | Telomere healing | Pf3D7_08_v3:1373732 | Cambodia (1) | 1 | 1373732 |
| 1 | Telomere healing | Pf3D7_08_v3:1374280 | Sudan (1) | 1 | 1374280 |
| 2 | Telomere healing | Pf3D7_08_v3:1374462 | Indonesia (5) | 5 | 1374462 |
| 3 | Telomere healing | Pf3D7_08_v3:1374932 | Peru (2) | 2 | 1374932 |
| 4 | Telomere healing | Pf3D7_08_v3:1374986 | Peru (6) | 6 | 1374986 |
A repeat of the same look-up for hrp3.
# Group samples by Deletion type and HRP3_breakpoint
# Apply breakpoint_agg to count samples with deletion in each country
df_hrp3 = (
df_samples[
df_samples['QC pass']
& ( df_samples['HRP3'] == 'del' )
]
.rename(columns={'HRP3_deletion_type': 'Deletion type'})
.groupby(['Deletion type', 'HRP3_breakpoint'])
.apply(breakpoint_agg)
.reset_index()
)
# Seperate coordinate value from chromosome
df_hrp3['breakpoint'] = df_hrp3['HRP3_breakpoint'].apply(lambda x: x.split(':')[1])
# Print min and max coordinates
print(f"HRP3 min breakpoint = {df_hrp3['breakpoint'].min()}")
print(f"HRP3 max breakpoint = {df_hrp3['breakpoint'].max()}")
df_hrp3
HRP3 min breakpoint = 2800004-2807159
HRP3 max breakpoint = 2841120
| Deletion type | HRP3_breakpoint | Countries | Samples with deletion | breakpoint | |
|---|---|---|---|---|---|
| 0 | Chromosome 11 recombination | Pf3D7_13_v3:2800004-2807159 | Thailand (1), Ghana (1), Indonesia (40), Peru ... | 151 | 2800004-2807159 |
| 1 | Chromosome 5 recombination | Pf3D7_13_v3:2835587-2835612 | Cambodia (20), Vietnam (1) | 21 | 2835587-2835612 |
| 2 | Telomere healing | Pf3D7_13_v3:2811525 | India (1) | 1 | 2811525 |
| 3 | Telomere healing | Pf3D7_13_v3:2812344 | Sudan (1) | 1 | 2812344 |
| 4 | Telomere healing | Pf3D7_13_v3:2815249 | Tanzania (1) | 1 | 2815249 |
| 5 | Telomere healing | Pf3D7_13_v3:2822480 | Ghana (1) | 1 | 2822480 |
| 6 | Telomere healing | Pf3D7_13_v3:2823645 | Kenya (1) | 1 | 2823645 |
| 7 | Telomere healing | Pf3D7_13_v3:2830952 | Cambodia (7) | 7 | 2830952 |
| 8 | Telomere healing | Pf3D7_13_v3:2832080 | Democratic Republic of the Congo (1) | 1 | 2832080 |
| 9 | Telomere healing | Pf3D7_13_v3:2834604 | Vietnam (1) | 1 | 2834604 |
| 10 | Telomere healing | Pf3D7_13_v3:2835532 | Thailand (1) | 1 | 2835532 |
| 11 | Telomere healing | Pf3D7_13_v3:2837145 | Vietnam (7) | 7 | 2837145 |
| 12 | Telomere healing | Pf3D7_13_v3:2837392 | Cambodia (2), Laos (1) | 3 | 2837392 |
| 13 | Telomere healing | Pf3D7_13_v3:2838654 | Indonesia (2) | 2 | 2838654 |
| 14 | Telomere healing | Pf3D7_13_v3:2841024 | Thailand (1) | 1 | 2841024 |
| 15 | Telomere healing | Pf3D7_13_v3:2841120 | Indonesia (1) | 1 | 2841120 |
It seems that Chromosome 11 recombination breakpoints are observed in multiple countries which motivates us to print the full list of countries.
# Locate the first row by using the index
df_hrp3.iloc[0]['Countries']
'Thailand (1), Ghana (1), Indonesia (40), Peru (15), Bangladesh (1), Vietnam (2), Colombia (50), Ethiopia (9), Senegal (6), Laos (14), Cambodia (3), Sudan (5), Mali (1), Gambia (3)'
Given the fact that many of these events result in the deletion of other genes in addition to hrp2 and hrp3.
We could have a look at which genes are present within the range of breakpoints before including them in the plot.
# Genes in hrp2 breakpoints
df_gff.loc[
( df_gff['chrom'] == 'Pf3D7_08_v3' )
& ( df_gff['start'] <= 1375500 )
& ( df_gff['end'] >= 1364000 )
]
| chrom | db | type | start | end | u1 | strand | u2 | id | |
|---|---|---|---|---|---|---|---|---|---|
| 15534 | Pf3D7_08_v3 | chado | three_prime_UTR | 1364640 | 1365466 | . | - | . | ID=PF3D7_0831700.1:3UTR;Parent=PF3D7_0831700.1 |
| 15535 | Pf3D7_08_v3 | chado | gene | 1364640 | 1369862 | . | - | . | ID=PF3D7_0831700;Name=HSP70x;previous_systemat... |
| 15536 | Pf3D7_08_v3 | chado | mRNA | 1364640 | 1369862 | . | - | . | ID=PF3D7_0831700.1;comment=HSP70-x can be knoc... |
| 15537 | Pf3D7_08_v3 | chado | CDS | 1365467 | 1367506 | . | - | 0 | ID=PF3D7_0831700.1:exon:1;Parent=PF3D7_0831700.1 |
| 15538 | Pf3D7_08_v3 | chado | polypeptide | 1365467 | 1367506 | . | - | . | ID=PF3D7_0831700.1:pep;Derives_from=PF3D7_0831... |
| 15539 | Pf3D7_08_v3 | chado | five_prime_UTR | 1367507 | 1367640 | . | - | . | ID=PF3D7_0831700.1:5UTR:1;Parent=PF3D7_0831700.1 |
| 15540 | Pf3D7_08_v3 | chado | five_prime_UTR | 1368649 | 1369862 | . | - | . | ID=PF3D7_0831700.1:5UTR;Parent=PF3D7_0831700.1 |
| 15541 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1371847 | 1372100 | . | + | . | ID=PF3D7_0831750.1:exon:2;Parent=PF3D7_0831750.1 |
| 15542 | Pf3D7_08_v3 | chado | pseudogene | 1371847 | 1372720 | . | + | . | ID=PF3D7_0831750 |
| 15543 | Pf3D7_08_v3 | chado | pseudogenic_transcript | 1371847 | 1372720 | . | + | . | ID=PF3D7_0831750.1;Parent=PF3D7_0831750;Dbxref... |
| 15544 | Pf3D7_08_v3 | chado | polypeptide | 1371847 | 1372720 | . | + | . | ID=PF3D7_0831750.1:pep;Derives_from=PF3D7_0831... |
| 15545 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1372103 | 1372223 | . | + | . | ID=PF3D7_0831750.1:exon:3;Parent=PF3D7_0831750.1 |
| 15546 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1372225 | 1372291 | . | + | . | ID=PF3D7_0831750.1:exon:4;Parent=PF3D7_0831750.1 |
| 15547 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1372294 | 1372577 | . | + | . | ID=PF3D7_0831750.1:exon:5;Parent=PF3D7_0831750.1 |
| 15548 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1372579 | 1372667 | . | + | . | ID=PF3D7_0831750.1:exon:6;Parent=PF3D7_0831750.1 |
| 15549 | Pf3D7_08_v3 | chado | pseudogenic_exon | 1372669 | 1372720 | . | + | . | ID=PF3D7_0831750.1:exon:7;Parent=PF3D7_0831750.1 |
| 15550 | Pf3D7_08_v3 | chado | three_prime_UTR | 1373212 | 1374235 | . | - | . | ID=PF3D7_0831800.1:3UTR;Parent=PF3D7_0831800.1 |
| 15551 | Pf3D7_08_v3 | chado | gene | 1373212 | 1376988 | . | - | . | ID=PF3D7_0831800;Name=HRP2;previous_systematic... |
| 15552 | Pf3D7_08_v3 | chado | mRNA | 1373212 | 1376988 | . | - | . | ID=PF3D7_0831800.1;comment=HRP2%2C which is on... |
| 15553 | Pf3D7_08_v3 | chado | CDS | 1374236 | 1375084 | . | - | 0 | ID=PF3D7_0831800.1:exon:3;Parent=PF3D7_0831800.1 |
| 15554 | Pf3D7_08_v3 | chado | polypeptide | 1374236 | 1375299 | . | - | . | ID=PF3D7_0831800.1:pep;Derives_from=PF3D7_0831... |
| 15555 | Pf3D7_08_v3 | chado | CDS | 1375231 | 1375299 | . | - | 0 | ID=PF3D7_0831800.1:exon:2;Parent=PF3D7_0831800.1 |
| 15556 | Pf3D7_08_v3 | chado | five_prime_UTR | 1375300 | 1376988 | . | - | . | ID=PF3D7_0831800.1:5UTR;Parent=PF3D7_0831800.1 |
# Genes in hrp3 breakpoints
pd.options.display.max_rows = 100
df_gff.loc[
( df_gff['chrom'] == 'Pf3D7_13_v3' )
& ( df_gff['start'] <= 2845000 )
& ( df_gff['end'] >= 2795000 )
]
| chrom | db | type | start | end | u1 | strand | u2 | id | |
|---|---|---|---|---|---|---|---|---|---|
| 34303 | Pf3D7_13_v3 | chado | gene | 2796119 | 2797144 | . | + | . | ID=PF3D7_1370800;previous_systematic_id=PF13TR... |
| 34304 | Pf3D7_13_v3 | chado | ncRNA | 2796119 | 2797144 | . | + | . | ID=PF3D7_1370800.1;comment=Barrell%2C March 20... |
| 34305 | Pf3D7_13_v3 | chado | CDS | 2796119 | 2797144 | . | + | 0 | ID=PF3D7_1370800.1:exon:1;Parent=PF3D7_1370800.1 |
| 34306 | Pf3D7_13_v3 | chado | gene | 2797507 | 2798103 | . | + | . | ID=PF3D7_1370900;previous_systematic_id=PF13TR... |
| 34307 | Pf3D7_13_v3 | chado | ncRNA | 2797507 | 2798103 | . | + | . | ID=PF3D7_1370900.1;comment=Barrell%2C March 20... |
| 34308 | Pf3D7_13_v3 | chado | CDS | 2797507 | 2798103 | . | + | 0 | ID=PF3D7_1370900.1:exon:1;Parent=PF3D7_1370900.1 |
| 34309 | Pf3D7_13_v3 | chado | gene | 2800004 | 2802154 | . | + | . | ID=PF3D7_1371000;previous_systematic_id=MAL13_... |
| 34310 | Pf3D7_13_v3 | chado | rRNA | 2800004 | 2802154 | . | + | . | ID=PF3D7_1371000.1;literature=PMID:17901154;Pa... |
| 34311 | Pf3D7_13_v3 | chado | CDS | 2800004 | 2802154 | . | + | 0 | ID=PF3D7_1371000.1:exon:3;Parent=PF3D7_1371000.1 |
| 34312 | Pf3D7_13_v3 | chado | gene | 2802527 | 2802686 | . | + | . | ID=PF3D7_1371200;previous_systematic_id=MAL13_... |
| 34313 | Pf3D7_13_v3 | chado | rRNA | 2802527 | 2802686 | . | + | . | ID=PF3D7_1371200.1;literature=PMID:17901154;Pa... |
| 34314 | Pf3D7_13_v3 | chado | CDS | 2802527 | 2802686 | . | + | 0 | ID=PF3D7_1371200.1:exon:1;Parent=PF3D7_1371200.1 |
| 34315 | Pf3D7_13_v3 | chado | gene | 2802945 | 2807159 | . | + | . | ID=PF3D7_1371300;previous_systematic_id=MAL13_... |
| 34316 | Pf3D7_13_v3 | chado | rRNA | 2802945 | 2807159 | . | + | . | ID=PF3D7_1371300.1;literature=PMID:17901154;Pa... |
| 34317 | Pf3D7_13_v3 | chado | CDS | 2802945 | 2807159 | . | + | 0 | ID=PF3D7_1371300.1:exon:3;Parent=PF3D7_1371300.1 |
| 34318 | Pf3D7_13_v3 | chado | three_prime_UTR | 2808200 | 2808562 | . | - | . | ID=PF3D7_1371500.1:3UTR;Parent=PF3D7_1371500.1 |
| 34319 | Pf3D7_13_v3 | chado | gene | 2808200 | 2810256 | . | - | . | ID=PF3D7_1371500;previous_systematic_id=MAL13P... |
| 34320 | Pf3D7_13_v3 | chado | mRNA | 2808200 | 2810256 | . | - | . | ID=PF3D7_1371500.1;Parent=PF3D7_1371500;Dbxref... |
| 34321 | Pf3D7_13_v3 | chado | CDS | 2808563 | 2809000 | . | - | 0 | ID=PF3D7_1371500.1:exon:1;Parent=PF3D7_1371500.1 |
| 34322 | Pf3D7_13_v3 | chado | polypeptide | 2808563 | 2809222 | . | - | . | ID=PF3D7_1371500.1:1:pep;Derives_from=PF3D7_13... |
| 34323 | Pf3D7_13_v3 | chado | CDS | 2809154 | 2809222 | . | - | 0 | ID=PF3D7_1371500.1:exon:2;Parent=PF3D7_1371500.1 |
| 34324 | Pf3D7_13_v3 | chado | five_prime_UTR | 2809223 | 2810256 | . | - | . | ID=PF3D7_1371500.1:5UTR;Parent=PF3D7_1371500.1 |
| 34325 | Pf3D7_13_v3 | chado | five_prime_UTR | 2811277 | 2821077 | . | + | . | ID=PF3D7_1371700.1:5UTR;Parent=PF3D7_1371700.1 |
| 34326 | Pf3D7_13_v3 | chado | gene | 2811277 | 2824446 | . | + | . | ID=PF3D7_1371700;previous_systematic_id=MAL13P... |
| 34327 | Pf3D7_13_v3 | chado | mRNA | 2811277 | 2824446 | . | + | . | ID=PF3D7_1371700.1;literature=PMID:15752424,PM... |
| 34328 | Pf3D7_13_v3 | chado | pseudogenic_exon | 2811706 | 2812263 | . | + | . | ID=PF3D7_1371600.1:exon:2;Parent=PF3D7_1371600.1 |
| 34329 | Pf3D7_13_v3 | chado | pseudogene | 2811706 | 2820270 | . | + | . | ID=PF3D7_1371600;Name=EBL1;previous_systematic... |
| 34330 | Pf3D7_13_v3 | chado | pseudogenic_transcript | 2811706 | 2820270 | . | + | . | ID=PF3D7_1371600.1;literature=PMID:15760663,PM... |
| 34331 | Pf3D7_13_v3 | chado | polypeptide | 2811706 | 2820270 | . | + | . | ID=PF3D7_1371600.1:pep;Derives_from=PF3D7_1371... |
| 34332 | Pf3D7_13_v3 | chado | pseudogenic_exon | 2812266 | 2819628 | . | + | . | ID=PF3D7_1371600.1:exon:3;Parent=PF3D7_1371600.1 |
| 34333 | Pf3D7_13_v3 | chado | pseudogenic_exon | 2819764 | 2819851 | . | + | . | ID=PF3D7_1371600.1:exon:1;Parent=PF3D7_1371600.1 |
| 34334 | Pf3D7_13_v3 | chado | pseudogenic_exon | 2820015 | 2820088 | . | + | . | ID=PF3D7_1371600.1:exon:4;Parent=PF3D7_1371600.1 |
| 34335 | Pf3D7_13_v3 | chado | pseudogenic_exon | 2820227 | 2820270 | . | + | . | ID=PF3D7_1371600.1:exon:5;Parent=PF3D7_1371600.1 |
| 34336 | Pf3D7_13_v3 | chado | CDS | 2821078 | 2821173 | . | + | 0 | ID=PF3D7_1371700.1:exon:2;Parent=PF3D7_1371700.1 |
| 34337 | Pf3D7_13_v3 | chado | polypeptide | 2821078 | 2823292 | . | + | . | ID=PF3D7_1371700.1:pep;Derives_from=PF3D7_1371... |
| 34338 | Pf3D7_13_v3 | chado | CDS | 2821278 | 2822786 | . | + | 0 | ID=PF3D7_1371700.1:exon:3;Parent=PF3D7_1371700.1 |
| 34339 | Pf3D7_13_v3 | chado | CDS | 2823212 | 2823292 | . | + | 0 | ID=PF3D7_1371700.1:exon:4;Parent=PF3D7_1371700.1 |
| 34340 | Pf3D7_13_v3 | chado | three_prime_UTR | 2823251 | 2824301 | . | - | . | ID=PF3D7_1371800.1:3UTR;Parent=PF3D7_1371800.1 |
| 34341 | Pf3D7_13_v3 | chado | gene | 2823251 | 2825967 | . | - | . | ID=PF3D7_1371800;previous_systematic_id=MAL13P... |
| 34342 | Pf3D7_13_v3 | chado | mRNA | 2823251 | 2825967 | . | - | . | ID=PF3D7_1371800.1;Parent=PF3D7_1371800;Dbxref... |
| 34343 | Pf3D7_13_v3 | chado | three_prime_UTR | 2823293 | 2824446 | . | + | . | ID=PF3D7_1371700.1:3UTR;Parent=PF3D7_1371700.1 |
| 34344 | Pf3D7_13_v3 | chado | CDS | 2824302 | 2825552 | . | - | 0 | ID=PF3D7_1371800.1:exon:3;Parent=PF3D7_1371800.1 |
| 34345 | Pf3D7_13_v3 | chado | polypeptide | 2824302 | 2825852 | . | - | . | ID=PF3D7_1371800.1:pep;Derives_from=PF3D7_1371... |
| 34346 | Pf3D7_13_v3 | chado | CDS | 2825781 | 2825852 | . | - | 0 | ID=PF3D7_1371800.1:exon:2;Parent=PF3D7_1371800.1 |
| 34347 | Pf3D7_13_v3 | chado | five_prime_UTR | 2825853 | 2825967 | . | - | . | ID=PF3D7_1371800.1:5UTR;Parent=PF3D7_1371800.1 |
| 34348 | Pf3D7_13_v3 | chado | five_prime_UTR | 2829530 | 2829855 | . | + | . | ID=PF3D7_1371900.1:5UTR;Parent=PF3D7_1371900.1 |
| 34349 | Pf3D7_13_v3 | chado | gene | 2829530 | 2830830 | . | + | . | ID=PF3D7_1371900;previous_systematic_id=MAL13P... |
| 34350 | Pf3D7_13_v3 | chado | mRNA | 2829530 | 2830830 | . | + | . | ID=PF3D7_1371900.1;Parent=PF3D7_1371900;Dbxref... |
| 34351 | Pf3D7_13_v3 | chado | CDS | 2829856 | 2829927 | . | + | 0 | ID=PF3D7_1371900.1:exon:1;Parent=PF3D7_1371900.1 |
| 34352 | Pf3D7_13_v3 | chado | polypeptide | 2829856 | 2830669 | . | + | . | ID=PF3D7_1371900.1:pep;Derives_from=PF3D7_1371... |
| 34353 | Pf3D7_13_v3 | chado | CDS | 2830109 | 2830669 | . | + | 0 | ID=PF3D7_1371900.1:exon:2;Parent=PF3D7_1371900.1 |
| 34354 | Pf3D7_13_v3 | chado | three_prime_UTR | 2830670 | 2830830 | . | + | . | ID=PF3D7_1371900.1:3UTR;Parent=PF3D7_1371900.1 |
| 34355 | Pf3D7_13_v3 | chado | five_prime_UTR | 2832623 | 2832951 | . | + | . | ID=PF3D7_1372000.1:5UTR;Parent=PF3D7_1372000.1 |
| 34356 | Pf3D7_13_v3 | chado | gene | 2832623 | 2835439 | . | + | . | ID=PF3D7_1372000;previous_systematic_id=MAL13P... |
| 34357 | Pf3D7_13_v3 | chado | mRNA | 2832623 | 2835439 | . | + | . | ID=PF3D7_1372000.1;comment=PHIST domain protei... |
| 34358 | Pf3D7_13_v3 | chado | CDS | 2832952 | 2833086 | . | + | 0 | ID=PF3D7_1372000.1:exon:1;Parent=PF3D7_1372000.1 |
| 34359 | Pf3D7_13_v3 | chado | polypeptide | 2832952 | 2834322 | . | + | . | ID=PF3D7_1372000.1:pep;Derives_from=PF3D7_1372... |
| 34360 | Pf3D7_13_v3 | chado | CDS | 2833204 | 2834322 | . | + | 0 | ID=PF3D7_1372000.1:exon:2;Parent=PF3D7_1372000.1 |
| 34361 | Pf3D7_13_v3 | chado | three_prime_UTR | 2834323 | 2835439 | . | + | . | ID=PF3D7_1372000.1:3UTR;Parent=PF3D7_1372000.1 |
| 34362 | Pf3D7_13_v3 | chado | five_prime_UTR | 2835756 | 2837052 | . | + | . | ID=PF3D7_1372100.1:5UTR;Parent=PF3D7_1372100.1 |
| 34363 | Pf3D7_13_v3 | chado | gene | 2835756 | 2839580 | . | + | . | ID=PF3D7_1372100;Name=GEXP04;previous_systemat... |
| 34364 | Pf3D7_13_v3 | chado | mRNA | 2835756 | 2839580 | . | + | . | ID=PF3D7_1372100.1;comment=Barrell%2C October ... |
| 34365 | Pf3D7_13_v3 | chado | CDS | 2837053 | 2837136 | . | + | 0 | ID=PF3D7_1372100.1:exon:1;Parent=PF3D7_1372100.1 |
| 34366 | Pf3D7_13_v3 | chado | polypeptide | 2837053 | 2839058 | . | + | . | ID=PF3D7_1372100.1:pep;Derives_from=PF3D7_1372... |
| 34367 | Pf3D7_13_v3 | chado | CDS | 2837313 | 2839058 | . | + | 0 | ID=PF3D7_1372100.1:exon:2;Parent=PF3D7_1372100.1 |
| 34368 | Pf3D7_13_v3 | chado | three_prime_UTR | 2839059 | 2839580 | . | + | . | ID=PF3D7_1372100.1:3UTR;Parent=PF3D7_1372100.1 |
| 34369 | Pf3D7_13_v3 | chado | three_prime_UTR | 2840236 | 2840726 | . | - | . | ID=PF3D7_1372200.1:3UTR;Parent=PF3D7_1372200.1 |
| 34370 | Pf3D7_13_v3 | chado | gene | 2840236 | 2842840 | . | - | . | ID=PF3D7_1372200;Name=HRPIII;previous_systemat... |
| 34371 | Pf3D7_13_v3 | chado | mRNA | 2840236 | 2842840 | . | - | . | ID=PF3D7_1372200.1;comment=sub-telomeric gene ... |
| 34372 | Pf3D7_13_v3 | chado | CDS | 2840727 | 2841485 | . | - | 0 | ID=PF3D7_1372200.1:exon:1;Parent=PF3D7_1372200.1 |
| 34373 | Pf3D7_13_v3 | chado | polypeptide | 2840727 | 2841703 | . | - | . | ID=PF3D7_1372200.1:pep;Derives_from=PF3D7_1372... |
| 34374 | Pf3D7_13_v3 | chado | CDS | 2841635 | 2841703 | . | - | 0 | ID=PF3D7_1372200.1:exon:2;Parent=PF3D7_1372200.1 |
| 34375 | Pf3D7_13_v3 | chado | five_prime_UTR | 2841704 | 2842840 | . | - | . | ID=PF3D7_1372200.1:5UTR;Parent=PF3D7_1372200.1 |
| 34376 | Pf3D7_13_v3 | chado | five_prime_UTR | 2843157 | 2845766 | . | + | . | ID=PF3D7_1372300.1:5UTR;Parent=PF3D7_1372300.1 |
| 34377 | Pf3D7_13_v3 | chado | gene | 2843157 | 2847557 | . | + | . | ID=PF3D7_1372300 |
| 34378 | Pf3D7_13_v3 | chado | mRNA | 2843157 | 2847557 | . | + | . | ID=PF3D7_1372300.1;Parent=PF3D7_1372300;Dbxref... |
Create figure¶
This intricate figure serves to map deletion breakpoints in hrp2 and hrp3 across various countries. The x-axis of the figure displays the deletion breakpoints, while the y-axis shows the countries along with the number of breakpoints they exhibit.
The figure comprises two sections: hrp2 (positioned at the top, subplots 1-5) and hrp3 (located at the bottom, subplots 6-11), consisting of a total of 11 subplots.
We start by defining distinct colour codes for each genomic region that we will annotate.
# Create a dictionary with distinct colour codes
figure_colours = collections.OrderedDict()
figure_colours['chr_8_13'] = '#377eb8'
figure_colours['chr_11'] = '#984ea3'
figure_colours['chr_5'] = '#ff7f00'
figure_colours['similar_sequence'] = '#ffff33'
figure_colours['hrp_genes'] = '#e41a1c'
figure_colours['rrna_genes'] = '#4daf4a'
figure_colours['other_genes'] = 'black'
figure_colours['pseudogenes'] = 'grey'
# Full figure
fig, axs = plt.subplots(11, 1, figsize=(9, 9), gridspec_kw={'height_ratios': [1, 7, 10, 6, 12, 1, 7, 32, 18, 12, 12], 'hspace': 0})
### HRP2
# Set the minimum and maximum positions for hrp2
min_pos = 1371500
max_pos = 1375500
## Subplot 1: Title and Chromosome Region
# Set the title
axs[0].set_title('Pf3D7_08_v3')
# Configure the x-axis properties
axs[0].set_xticks([])
axs[0].set_xlabel(None)
# Configure the y-axis properties
axs[0].set_yticks([])
# Set the x-axis limits to match the genomic region of 'Pf3D7_08_v3'
axs[0].set_xlim(int(df_chroms.loc['Pf3D7_08_v3', 'start']), int(df_chroms.loc['Pf3D7_08_v3', 'end']))
# Define the background colour of the subplot
axs[0].set_facecolor(figure_colours['chr_8_13'])
# Add a vertical span between 'min_pos' and 'max_pos' with a white background
axs[0].axvspan(min_pos, max_pos, color='white')
## Subplot 2: Chromosome Boundaries
# Configure x and y axis ticks
axs[1].set_xticks([])
axs[1].set_yticks([])
# Set the x-axis limits to match the genomic region of 'Pf3D7_08_v3'
axs[1].set_xlim(int(df_chroms.loc['Pf3D7_08_v3', 'start']), int(df_chroms.loc['Pf3D7_08_v3', 'end']))
# Define the y-axis limits
axs[1].set_ylim(0, 1)
# Plot horizontal lines marking the start and end positions of 'Pf3D7_08_v3'
axs[1].plot([int(df_chroms.loc['Pf3D7_08_v3', 'start']), min_pos], [0, 1], '-', color='black')
axs[1].plot([int(df_chroms.loc['Pf3D7_08_v3', 'end']), max_pos], [0, 1], '-', color='black')
# Hide the left and right spines for a cleaner appearance
axs[1].spines['left'].set_visible(False)
axs[1].spines['right'].set_visible(False)
# Subplot 3: Deletion Breakpoints by Country
# Create lists for y-axis labels and positions
ylabels = []
yposes = []
# Loop through the sorted df_hrp2
for ypos, row in df_hrp2.sort_values('breakpoint', ascending=False).iterrows():
yposes.append(ypos)
# Plot horizontal lines connecting min_pos to each hrp2 deletion breakpoint
axs[2].plot((min_pos, row['breakpoint']), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_8_13'])
# Plot circular markers for each breakpoint position
axs[2].plot(row['breakpoint'] + np.arange(20*2, 101*2, 40*2), [ypos]*3, 'o', color=figure_colours['chr_8_13'])
# Add country names to the y-labels list
ylabels.append(f"{row['Countries']}")
# Set y-axis ticks and labels to correspond to country names
axs[2].set_yticks(yposes)
axs[2].set_yticklabels(ylabels)
# Set x-axis limits to cover the specified region between min_pos and max_pos
axs[2].set_xlim(min_pos, max_pos)
# Adjust y-axis limits to include space for data visualization
axs[2].set_ylim(min(yposes) - 0.5, max(yposes) + 0.5)
# Hide x-axis ticks for a cleaner appearance
axs[2].set_xticks([])
## Subplot 4: Genes on the x-axis
# Set the x-axis limits to cover the specified genomic region between min_pos and max_pos
axs[3].set_xlim(min_pos, max_pos)
# Set the y-axis limits to display a region between 0 and 1
axs[3].set_ylim(0, 1)
# Iterate through df_gff to process different gene annotations
for ix, row in df_gff.loc[
(df_gff['chrom'] == 'Pf3D7_08_v3')
& (df_gff['start'] <= max_pos)
& (df_gff['end'] >= min_pos)
& (df_gff['type'] == 'CDS')
].iterrows():
# Determine the color based on the genomic position
if row['start'] >= 1373212 and row['end'] <= 1376988:
color = figure_colours['hrp_genes']
else:
color = figure_colours['other_genes']
# Plot CDS features with specified line properties and color
axs[3].plot((row['start'], row['end']), (0.75, 0.75), linewidth=6, solid_capstyle='butt', color=color)
# Iterate through polypeptide
for ix, row in df_gff.loc[
(df_gff['chrom'] == 'Pf3D7_08_v3')
& (df_gff['start'] <= max_pos)
& (df_gff['end'] >= min_pos)
& (df_gff['type'] == 'polypeptide')
].iterrows():
if row['start'] >= 1373212 and row['end'] <= 1376988:
color = figure_colours['hrp_genes']
else:
color = figure_colours['other_genes']
# Plot polypeptide features with different line properties and color
axs[3].plot((row['start'], row['end']), (0.75, 0.75), linewidth=2, solid_capstyle='butt', color=color)
# Iterate through pseudogene
for ix, row in df_gff.loc[
(df_gff['chrom'] == 'Pf3D7_08_v3')
& (df_gff['start'] <= max_pos)
& (df_gff['end'] >= min_pos)
& (df_gff['type'] == 'pseudogene')
].iterrows():
# Plot pseudogene features with specified line properties and color
axs[3].plot((row['start'], row['end']), (0.75, 0.75), linewidth=6, solid_capstyle='butt', color=figure_colours['pseudogenes'])
# Add text labels for specific genomic regions
axs[3].text((1375299 + 1374236) / 2, 0.25, '${hrp2}$', va='center', ha='center', size=8)
axs[3].text((1371847 + 1372720) / 2, 0.25, 'PF3D7_0831750 (pseudogene)', va='center', ha='center', size=8)
# Set y-axis ticks, labels, and x-axis ticks to maintain an organized representation
axs[3].set_yticks([])
axs[3].set_xticks([])
axs[3].set_ylabel('Genes', rotation=0, ha='right', va='center')
## Subplot 5: Ticks for Genomic Coordinates
# Set the x-axis limits to cover the specified genomic region between 'min_pos' and 'max_pos'
axs[4].set_xlim(min_pos, max_pos)
# Hide the left and right spines to create a cleaner appearance
axs[4].spines['left'].set_visible(False)
axs[4].spines['right'].set_visible(False)
# Configure the x-axis ticks at specific positions
axs[4].set_xticks([1372000, 1373000, 1374000, 1375000])
# Label the x-axis ticks with corresponding values
axs[4].set_xticklabels(["1.372 Mbp", "1.373 Mbp", "1.374 Mbp", "1.375 Mbp"])
# Remove y-axis ticks to maintain a clean look
axs[4].set_yticks([])
# Position x-axis ticks at the top
axs[4].xaxis.tick_top()
# Adjust the direction of x-axis ticks and add padding
axs[4].tick_params(axis="x", direction="in", pad=-20)
### HRP3
# Set the minimum and maximum positions for hrp3
min_pos = 2795000
max_pos = 2845000
## Subplot 6: Title and Chromosome Region
# Set the title
axs[5].set_title('Pf3D7_13_v3')
# Hide the x-axis ticks and labels
axs[5].set_xticks([])
axs[5].set_xlabel(None)
# Hide the y-axis ticks
axs[5].set_yticks([])
# Set the x-axis limits to cover the specified genomic region between the start and end positions of chromosome
axs[5].set_xlim(int(df_chroms.loc['Pf3D7_13_v3', 'start']), int(df_chroms.loc['Pf3D7_13_v3', 'end']))
# Customize the background color of the subplot
axs[5].set_facecolor(figure_colours['chr_8_13'])
# Create a white span on the plot to mark the genomic region of interest
axs[5].axvspan(min_pos, max_pos, color='white')
## Subplot 7: Chromosome Boundaries
# Hide the x-axis ticks and labels
axs[6].set_xticks([])
axs[6].set_yticks([])
# Set the x-axis limits to cover the specified genomic region between the start and end positions of 'Pf3D7_13_v3'
axs[6].set_xlim(int(df_chroms.loc['Pf3D7_13_v3', 'start']), int(df_chroms.loc['Pf3D7_13_v3', 'end']))
# Set the y-axis limits to cover a range from 0 to 1
axs[6].set_ylim(0, 1)
# Create lines to mark the start and end positions of the chromosome region with black color
axs[6].plot([int(df_chroms.loc['Pf3D7_13_v3', 'start']), min_pos], [0, 1], '-', color='black')
axs[6].plot([int(df_chroms.loc['Pf3D7_13_v3', 'end']), max_pos], [0, 1], '-', color='black')
# Hide the left and right spines to create a cleaner appearance
axs[6].spines['left'].set_visible(False)
axs[6].spines['right'].set_visible(False)
## Subplot 8: Deletion Breakpoints by Country
# Initialize lists for labels and positions
ylabels = []
yposes = []
# Iterate through df_hrp3 sorted by breakpoints
for ypos, row in df_hrp3.sort_values('breakpoint', ascending=False).iterrows():
yposes.append(ypos)
# Check the deletion type and apply different plotting styles accordingly
if row['Deletion type'] == 'Telomere healing':
breakpoint = int(row['breakpoint'])
# Plot the deletion breakpoint and add circular markers
axs[7].plot((min_pos, breakpoint), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_8_13'])
axs[7].plot(int(row['breakpoint']) + np.arange(20*25, 101*25, 40*25), [ypos]*3, 'o', color=figure_colours['chr_8_13'])
ylabels.append(f"{row['Countries']}")
if row['Deletion type'] == 'Chromosome 5 recombination':
breakpoint_start, breakpoint_end = [int(x) for x in row['breakpoint'].split('-')]
# Plot the breakpoints for Chromosome 5 recombination
axs[7].plot((min_pos, breakpoint_start), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_8_13'])
axs[7].plot((breakpoint_end, max_pos), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_5'])
ylabels.append(f"{row['Countries']}")
if row['Deletion type'] == 'Chromosome 11 recombination':
breakpoint_start, breakpoint_end = [int(x) for x in row['breakpoint'].split('-')]
# Plot the breakpoints for Chromosome 11 recombination
axs[7].plot((min_pos, breakpoint_start), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_8_13'])
axs[7].plot((breakpoint_end, max_pos), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_11'])
axs[7].plot((breakpoint_start, breakpoint_end), (ypos, ypos), linewidth=6, solid_capstyle='butt', color=figure_colours['similar_sequence'])
ylabels.append(f"14 countries (151)")
# Set the y-axis ticks and labels
axs[7].set_yticks(yposes)
axs[7].set_yticklabels(ylabels)
# Set x-axis limits, y-axis limits, and remove x-axis ticks
axs[7].set_xlim(min_pos, max_pos)
axs[7].set_ylim(min(yposes)-0.5, max(yposes)+0.5)
axs[7].set_xticks([])
## Subplot 9: Genes on the x-axis
bar_pos = 0.9
text_pos = 0.8
# Set the x-axis and y-axis limits
axs[8].set_xlim(min_pos, max_pos)
axs[8].set_ylim(0, 1)
# Iterate through GFF data filtering for relevant features
# Plot CDS features in distinct colors, depending on their location
for ix, row in df_gff.loc[
( df_gff['chrom'] == 'Pf3D7_13_v3' )
& ( df_gff['start'] <= max_pos )
& ( df_gff['end'] >= min_pos )
& ( df_gff['type'] == 'CDS' )
].iterrows():
if row['start'] >= 2840727 and row['end'] <= 2841703:
color = figure_colours['hrp_genes']
else:
color = figure_colours['other_genes']
axs[8].plot((row['start'], row['end']), (bar_pos, bar_pos), linewidth=6, solid_capstyle='butt', color=color)
# Plot polypeptide features with similar style, with specific colors
for ix, row in df_gff.loc[
( df_gff['chrom'] == 'Pf3D7_13_v3' )
& ( df_gff['start'] <= max_pos )
& ( df_gff['end'] >= min_pos )
& ( df_gff['type'] == 'polypeptide' )
].iterrows():
if row['start'] >= 2840727 and row['end'] <= 2841703:
color = figure_colours['hrp_genes']
else:
color = figure_colours['other_genes']
axs[8].plot((row['start'], row['end']), (bar_pos, bar_pos), linewidth=2, solid_capstyle='butt', color=color)
# Plot rRNA and pseudogene features
for ix, row in df_gff.loc[
( df_gff['chrom'] == 'Pf3D7_13_v3' )
& ( df_gff['start'] <= max_pos )
& ( df_gff['end'] >= min_pos )
& ( df_gff['type'] == 'rRNA' )
].iterrows():
axs[8].plot((row['start'], row['end']), (bar_pos, bar_pos), linewidth=6, solid_capstyle='butt', color=figure_colours['rrna_genes'])
# Annotate specific gene positions with labels
axs[8].text((2796119 + 2797144) / 2, text_pos, 'PF3D7_1370800', va='top', ha='center', size=8, rotation=90)
axs[8].text((2797507 + 2798103) / 2, text_pos, 'PF3D7_1370900', va='top', ha='center', size=8, rotation=90)
axs[8].text((2800004 + 2802154) / 2, text_pos, '18S rRNA', va='top', ha='center', size=8, rotation=90)
axs[8].text((2802527 + 2802686) / 2, text_pos, '5.8S rRNA', va='top', ha='center', size=8, rotation=90)
axs[8].text((2802945 + 2807159) / 2, text_pos, '28S rRNA', va='top', ha='center', size=8, rotation=90)
axs[8].text((2808200 + 2810256) / 2, text_pos, 'PF3D7_1371500', va='top', ha='center', size=8, rotation=90)
axs[8].text((2811706 + 2820270) / 2, text_pos, '${ebl1}$\n(pseudogene)', va='top', ha='center', size=8, rotation=90)
axs[8].text((2821078 + 2823292) / 2, text_pos, 'PF3D7_1371700', va='top', ha='center', size=8, rotation=90)
axs[8].text((2824302 + 2825852) / 2, text_pos, 'PF3D7_1371800', va='top', ha='center', size=8, rotation=90)
axs[8].text((2829856 + 2830669) / 2, text_pos, 'PF3D7_1371900', va='top', ha='center', size=8, rotation=90)
axs[8].text((2832952 + 2834322) / 2, text_pos, 'PF3D7_1372000', va='top', ha='center', size=8, rotation=90)
axs[8].text((2837053 + 2839058) / 2, text_pos, '${gexp04}$', va='top', ha='center', size=8, rotation=90)
axs[8].text((2840727 + 2841703) / 2, text_pos, '${hrp3}$', va='top', ha='center', size=8, rotation=90)
# Set y-axis ticks, customize x-axis labels, and add x-axis label and y-axis label
axs[8].set_yticks([])
axs[8].set_xticklabels(["", "2.80 Mbp", "2.81 Mbp", "2.82 Mbp", "2.83 Mbp", "2.84 Mbp"])
axs[8].set_xlabel('Genomic coordinates')
axs[8].set_ylabel('Genes', rotation=0, ha='right', va='center')
## Subplot 10: Ticks for Genomic Coordinates
# Set the x-axis limits
axs[9].set_xlim(min_pos, max_pos)
# Hide left and right spines to make the plot clean
axs[9].spines['left'].set_visible(False)
axs[9].spines['right'].set_visible(False)
# Define custom tick positions on the x-axis
axs[9].set_xticks([2800000, 2810000, 2820000, 2830000, 2840000])
# Set custom tick labels corresponding to genomic positions
axs[9].set_xticklabels(["2.80 Mbp", "2.81 Mbp", "2.82 Mbp", "2.83 Mbp", "2.84 Mbp"])
# Hide the y-axis ticks
axs[9].set_yticks([])
# Move the x-axis ticks to the top
axs[9].xaxis.tick_top()
# Adjust the direction and padding of x-axis ticks
axs[9].tick_params(axis="x", direction="in", pad=-20)
# Add a label to the x-axis indicating 'Genomic coordinates' at the center of the x-axis
axs[9].text((min_pos + max_pos) / 2, 0.5, 'Genomic coordinates', ha='center')
## Subplot 11: Legend
# Hide left, right, and bottom spines
axs[10].spines['left'].set_visible(False)
axs[10].spines['right'].set_visible(False)
axs[10].spines['bottom'].set_visible(False)
# Hide both x and y-axis ticks
axs[10].set_xticks([])
axs[10].set_yticks([])
# Create legend elements with color bars and labels
axs[10].plot((0, 0.1), (0.5, 0.5), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_5'])
axs[10].text(0.12, 0.5, 'Chr 5 sequence', va='center')
axs[10].plot((0, 0.1), (0.25, 0.25), linewidth=6, solid_capstyle='butt', color=figure_colours['chr_11'])
axs[10].text(0.12, 0.25, 'Chr 11 sequence', va='center')
axs[10].plot((0.38, 0.48), (0.5, 0.5), linewidth=6, solid_capstyle='butt', color=figure_colours['similar_sequence'])
axs[10].text(0.50, 0.5, 'Highly similar sequence on chr 13 and chr 11', va='center')
axs[10].plot(np.arange(0.405, 0.456, 0.025), [0.25]*3, 'o', color=figure_colours['chr_8_13'])
axs[10].text(0.50, 0.25, 'Telomeric repeat sequence', va='center')
# Set the limits for this subplot
axs[10].set_xlim(0, 1)
axs[10].set_ylim(0, 1)
# Ensure the figure layout is tidy
fig.tight_layout()
<ipython-input-16-28c0075a88af>:278: UserWarning: FixedFormatter should only be used together with FixedLocator
axs[8].set_xticklabels(["", "2.80 Mbp", "2.81 Mbp", "2.82 Mbp", "2.83 Mbp", "2.84 Mbp"])
Figure Legend: HRP deletion breakpoints. We see five different breakpoints resulting in the deletion of hrp2. Four of these are within exon 2 of the gene whereas the fifth is found between hrp2 and the pseudogene PF3D7_0831750. For all five events we see evidence of telomeric healing from reads that contain part Pf3D7_08_v3 sequence and part telomeric repeat sequence (GGGTTCA/GGGTTTA). We see 16 different breakpoints resulting in the deletion of hrp3. For fourteen of these we see evidence of telomeric healing. Note that many of these events result in the deletion of other genes in addition to hrp3. For twenty samples from Cambodia and a single sample from Vietnam we see evidence of a recombination with chromosome 5 which results in a hybrid chromosome comprising mostly chromosome 13 sequence but a small inverted section of an internal portion of chromosome 5 containing the gene mdr1. We also see evidence of a recombination with chromosome 11 which results in a hybrid chromosome comprising mostly chromosome 13 sequence but also a section of the 3’ end of chromosome 11. This is the most common deletion type, being seen in 151 samples from 14 different countries. Because the recombination occurs between highly similar sequences of a set of three orthologous ribosomal RNA genes found on both chromosomes, it is not possible to identify the exact breakpoints.
Save Figure¶
# You will need to authorise Google Colab access to Google Drive
drive.mount('/content/drive')
# This will send the file to your Google Drive, where you can download it from if needed
# Change the file path if you wish to send the file to a specific location
# Change the file name if you wish to call it something else
fig.savefig('/content/drive/My Drive/HRP_Deletions_Figure.pdf')
fig.savefig('/content/drive/My Drive/HRP_Deletions_Figure.png', dpi=480) # increase the dpi for higher resolution